Recommender systems are used for automatically presenting a recommendation relating to a content item, such as a product, a video, a journal, a TV program, a song etc. available online, to a user. As a user is confronted with a large amount of content items that are offered in various databases, he/she can have severe difficulties in finding content items of interest. A recommender system facilitates retrieving content items that are of interest for the user and avoid the need to perform complex searches. For instance, international patent application publication WO 2010/122448 A1 describes such a recommender system.
There are different approaches of designing a recommender system. One approach results in a content-based recommender system and another approach in a collaborative-filtering-based recommender system.
A content-based recommender system recommends a content item being represented as multiple features to a user, if this feature representation corresponds to the user's preferences, which are also expressed in terms of these features.
A collaborative-filtering-based recommender system analyses the purchase/viewing/listening history of a comparatively large set of users in order to identify a similarity between content items and a similarity between users. For instance, new content items offered in a content item database that are similar to items that a user likes are recommended to the user. Or, new items that are liked by some users that are similar to a certain user are recommended to the certain user. Usually, a collaborative-filtering-based recommender system does not require specific information on content items themselves other than purchase/viewing/listening information from users.
Besides recommendations that are solely and automatically generated by recommender systems, a user can submit a personal recommendation to one of his contacts. For instance, a presentation of a content item in the internet is often accompanied by a so-called “tell-a-friend” button that a user can click on and thereby submit a link, i.e. a web address, to the representation of the content item and eventually a personal note to one or more of his contacts.
Recommender systems are known that are adapted to promote such personal recommendations or semi-personal recommendations. For instance, a recommender system is known that recommends an item to a user, if one of the contacts of the user has positively rated the item. Such semi-personal recommendation can also be generated, if the contact of the user has only implicitly expressed that he likes a content item. For instance, a recommender system of the user recognises that the user likes or dislikes a certain content item and forwards this recognition in the form of a recommendation to all contacts of the user, thereby informing all contacts of the user that the user likes/dislikes a certain content item. The recognition, whether the user likes/dislikes a certain content item can also be based on an explicit rating the user has submitted in relation to this certain content item.